Heterogeneity by age and gender in the association of kidney function with mortality among patients with diabetes - analysis of diabetes registry in Singapore.

BACKGROUND: We aimed to explore the three-way interaction among age, gender, and kidney function on the risk of all-cause mortality and cardiovascular mortality among patients with type 2 diabetes (T2D). METHODS: In a retrospective cohort study, patients aged > 40 years with T2D with serum creatinine and urine albumin measured from 2013 to 2019 were included from a multi-institutional diabetes registry. The exposure was estimated glomerular filtration rate (eGFR), outcomes were all-cause mortality (primary outcome) and cardiovascular disease (CVD) mortality (secondary outcome). We applied multivariable cox proportional hazards regression analysis to compute the association between eGFR and mortality. RESULTS: A total of 36,556 patients were followed for up to 6 years during which 2492 (6.82%) died from all causes, and 690 (1.9%) died from CVD. We observed a significant three-way interaction (p = 0.021) among age (younger, < 65; older, ≥65 years), gender and eGFR for the risk of all-cause mortality. Using age- and gender-specific eGFR of 90 ml/min/1.73m2 as the reference point, the adjusted hazard rate (HR) (95% CI) for all-cause mortality at eGFR of 40 ml/min/1.73m2 was 3.70 (2.29 to 5.99) in younger women and 1.86 (1.08 to 3.19) in younger men. The corresponding adjusted HRs in older women and older men were 2.38 (2.02 to 2.82) and 2.18 (1.85 to 2.57), respectively. Similar results were observed for CVD deaths, although the three-way interaction was not statistically significant. Sensitivity analysis yielded similar results. CONCLUSIONS: In this T2D population, younger women with reduced kidney function might be more susceptible to higher risks of all-cause mortality and CVD mortality than younger men.

A real-life comparison of hypoglycaemia symptomatology between insulin-treated type 2 diabetes participants with and without impaired awareness of hypoglycaemia.

AIMS: We aim to compare and correlate Gold and Clarke questionnaire scores with hypoglycaemic symptomatic responses between insulin-treated type 2 diabetes participants with and without IAH in a real-life study. METHODS: Insulin-treated type 2 diabetes participants attending an outpatient diabetes clinic in Singapore were asked to complete the Gold and Clarke questionnaires, record capillary blood glucose (CBG) and hypoglycaemic symptoms for 4 weeks. RESULTS: Data were collected from 153 participants (M:F = 98:55) with mean age 61.0 ± 9.4 years, duration of diabetes 19.5 ± 8.8 years and HbA1c 68 ± 17 mmol/mol (8.4 ± 1.5%). Gold and Clarke methods classified 19.6% and 26.8% of participants with IAH, respectively. Using CBG threshold of <3 mmol/L, significantly greater proportion of participants with intact awareness were experiencing autonomic symptoms than those with IAH with either method (Gold: 69% vs. 18%, p = 0.006; Clarke: 85% vs. 46%, p = 0.010). Significantly greater proportion of participants with IAH experienced no hypoglycaemia symptoms than those with intact awareness (Gold: 3.4% vs. 36%, p = 0.015; Clarke: 3.7% vs. 31%, p = 0.031). Participants with IAH had significantly higher rates of severe hypoglycaemia in the preceding year compared to those without (Gold: 17% vs. 3.3%; Clarke: 15% vs. 2.7%, p = 0.012). CONCLUSIONS: Gold and Clarke questionnaires are appropriate tools in ascertaining IAH status in insulin-treated type 2 diabetes participants. This is the first time whereby the hypoglycaemia symptomology has robustly validated the Gold and Clarke questionnaire in insulin-treated type 2 diabetes participants.

Progressive endoplasmic reticulum stress over time due to human insulin gene mutation contributes to pancreatic beta cell dysfunction.

AIMS/HYPOTHESIS: We studied the effects of heterozygous human INS gene mutations on insulin secretion, endoplasmic reticulum (ER) stress and other mechanisms in both MIN6 and human induced pluripotent stem cells (hiPSC)-derived beta-like cells, as well as the effects of prolonged overexpression of mutant human INS in MIN6 cells. METHODS: We modelled the structure of mutant C109Y and G32V proinsulin computationally to examine the in silico effects. We then overexpressed either wild-type (WT), mutant (C109Y or G32V), or both WT and mutant human preproinsulin in MIN6 cells, both transiently and stably over several weeks. We measured the levels of human and rodent insulin secreted, and examined the transcript and protein levels of several ER stress and apoptotic markers. We also reprogrammed human donor fibroblasts heterozygous for the C109Y mutation into hiPSCs and differentiated these into pancreatic beta-like cells, which were subjected to single-cell RNA-sequencing and transcript and protein analyses for ER stress and apoptotic markers. RESULTS: The computational modelling studies, and short-term and long-term expression studies in beta cells, revealed the presence of ER stress, organelle changes and insulin processing defects, resulting in a decreased amount of insulin secreted but not the ability to secrete insulin. By 9 weeks of expression of mutant human INS, dominant-negative effects of mutant INS were evident and beta cell insulin secretory capacity declined. INS+/C109Y patient-derived beta-like cells and single-cell RNA-sequencing analyses then revealed compensatory upregulation in genes involved in insulin secretion, processing and inflammatory response. CONCLUSIONS/INTERPRETATION: The results provide deeper insights into the mechanisms of beta cell failure during INS mutation-mediated diabetes disease progression. Decreasing spliced X-box binding protein 1 (sXBP1) or inflammatory response could be avenues to restore the function of the remaining WT INS allele.

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE: The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS: The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS: The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS: Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

Inpatient glucose management programme in the Asian healthcare setting.

AIM: To implement an inpatient glucose management (IGM) programme in the general medical wards and evaluate its clinical efficacy. METHOD: Consecutive patients admitted to selected medical wards over a 12-month period were included in the IGM programme. All patients with ≥3 capillary blood glucose (CBG) readings (>10.0 mmol/L and/or <4.0 mmol/L) over a 24-hour period were identified electronically. A multidisciplinary team of diabetes care providers would provide specialist input to these patients. The control group comprised of consecutive patients admitted over the preceding 12 months. Outcome glucose measures include mean in-hospital glucose (MHG), mean patient-day glucose (MDG), proportion of CBG readings at predefined cut-offs and length of stay (LOS). RESULTS: Both the MHG and MDG were significantly lower following intervention (10.0±2.4 mmol/L vs 11.2±2.6 mmol/L, P<.001; 10.0±2.3 mmol/L vs 11.2±2.6 mmol/L, P<.001, respectively). Prevalence of hyperglycaemic events, defined by CBG >10.0 mmol/L, was significantly lower at 36.5% versus 51.6% (P<.001). Hypoglycaemic events of CBG <4.0 mmol/L remained infrequent at <1.0% before and after IGM programme. A greater proportion of glucose readings was controlled within the target range of 4.0-10.0 mmol/L (62.6% vs 47.6%, P<.001). With the IGM programme in place, more patients received scheduled CBG monitoring, and a significant shortening of mean LOS by 3.2 days was observed (P=.02). CONCLUSIONS: The IGM programme was effective in improving inpatient glycaemic monitoring and control in the general medical wards, with a significant reduction in LOS observed. These demonstrated the programme's potential to enhance quality and efficiency of patient care.

Hyperglycaemia attenuates in vivo reprogramming of pancreatic exocrine cells to beta cells in mice.

AIMS/HYPOTHESIS: Reprogramming of pancreatic exocrine to insulin-producing cells by viral delivery of the genes encoding transcription factors neurogenin-3 (Ngn3), pancreas/duodenum homeobox protein 1 (Pdx1) and MafA is an efficient method for reversing diabetes in murine models. The variables that modulate reprogramming success are currently ill-defined. METHODS: Here, we assess the impact of glycaemia on in vivo reprogramming in a mouse model of streptozotocin-induced beta cell ablation, using subsequent islet transplantation or insulin pellet implantation for creation of groups with differing levels of glycaemia before viral delivery of transcription factors. RESULTS: We observed that hyperglycaemia significantly impaired reprogramming of exocrine to insulin-producing cells in their quantity, differentiation status and function. With hyperglycaemia, the reprogramming of acinar towards beta cells was less complete. Moreover, inflammatory tissue changes within the exocrine pancreas including macrophage accumulation were found, which may represent the tissue's response to clear the pancreas from insufficiently reprogrammed cells. CONCLUSIONS/INTERPRETATION: Our findings shed light on normoglycaemia as a prerequisite for optimal reprogramming success in a diabetes model, which might be important in other tissue engineering approaches and disease models, potentially facilitating their translational applications.

Permanent neonatal diabetes in siblings with novel C109Y INS mutation transmitted by an unaffected parent with somatic mosaicism.

Mutations involving the insulin (INS) gene are a common cause of permanent neonatal diabetes (PND). Although INS mutations typically occur de novo and germline INS mutations transmitted to offspring by unaffected parents has been described, somatic mosaicism in a parent with an INS mutation has not been previously reported. We describe two siblings (one brother and one sister) with PND (26- and 19-yr old diagnosed at 3 and 7 months old, respectively), whose parents were unaffected. We performed genetic analysis of leukocyte DNA for this family. Both patients were found to carry the novel heterozygous c.326G>A substitution in exon 3 of INS, resulting in a p.C109Y change of the insulin protein. Analyses of leukocyte DNA from the parents revealed low level mutation in the sequencing trace of the father, raising the possibility of somatic mosaicism. Real-time polymerase chain reaction (PCR) analysis showed he had approximately 73% of the mutant allele relative to his affected son. This first report of somatic mosaicism in an unaffected parent with an INS mutation suggests that parental mosaicism may be responsible for the transmission of PND in patients with de novo INS mutations. As such, appropriate counseling for recurrent risks should be considered and we recommend that molecular genetic testing for future siblings at birth should be offered to the parents of children with INS mutation.

Kidney function trajectories, associated factors, and outcomes in multiethnic Asian patients with type 2 diabetes.

BACKGROUND: We examined the trajectory of estimated glomerular filtrate rate (eGFR), associated risk factors, and its relationship with end-stage kidney disease (ESKD) among a multiethnic patient population with type 2 diabetes in Singapore. METHODS: A follow-up study included 62 080 individuals with type 2 diabetes aged ≥18 years in a multi-institutional SingHealth Diabetes Registry between 2013 and 2019. eGFR trajectories were analyzed using latent class linear mixed models. Factors associated with eGFR trajectories were evaluated using multinomial logistic regression. The association of eGFR trajectories with ESKD was assessed via competing risk models. RESULTS: Trajectory of kidney function, determined by eGFR, was nonlinear. The trajectory pattern was classified as stable initially then gradual decline (75%), progressive decline (21.9%), and rapid decline (3.1%). Younger age, female sex, Malay ethnicity, lower-income housing type, current smoking, higher glycated hemoglobin, lower low-density lipoprotein, higher triglyceride, uncontrolled blood pressure, albuminuria, cardiovascular disease, hypertension, and higher eGFR levels each were associated with progressive or rapid decline. Compared with the trajectory of stable initially then gradual eGFR decline, progressive decline increased the hazard of ESKD by 6.14-fold (95% confidence interval [CI]: 4.96-7.61)) and rapid decline by 82.55 folds (95% CI: 55.90-121.89). CONCLUSIONS: Three nonlinear trajectory classes of kidney function were identified among multiethnic individuals with type 2 diabetes in Singapore. About one in four individuals had a progressive or rapid decline in eGFR. Our results suggest that eGFR trajectories are correlated with multiple social and modifiable risk factors and inform the risk of ESKD.

A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus.

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.

Early repeat hospitalization for fluid overload in individuals with cardiovascular disease and risks: a retrospective cohort study.

AIMS: Fluid overload is a common manifestation of cardiovascular and kidney disease and a leading cause of hospitalizations. To identify patients at risk of recurrent severe fluid overload, we evaluated the incidence and risk factors associated with early repeat hospitalization for fluid overload among individuals with cardiovascular disease and risks. METHODS: Single-center retrospective cohort study of 3423 consecutive adults with an index hospitalization for fluid overload between January 2015 and December 2017 and had cardiovascular risks (older age, diabetes mellitus, hypertension, dyslipidemia, kidney disease, known cardiovascular disease), but excluded if lost to follow-up or eGFR < 15 ml/min/1.73 m2. The outcome was early repeat hospitalization for fluid overload within 30 days of discharge. RESULTS: The mean age was 73.9 ± 11.6 years and eGFR was 54.1 ± 24.6 ml/min/1.73 m2 at index hospitalization. Early repeat hospitalization for fluid overload occurred in 291 patients (8.5%). After adjusting for demographics, comorbidities, clinical parameters during index hospitalization and medications at discharge, cardiovascular disease (adjusted odds ratio, OR 1.66, 95% CI 1.27-2.17), prior hospitalization for fluid overload within 3 months (OR 2.52, 95% CI 1.17-5.44), prior hospitalization for any cause in within 6 months (OR 1.33, 95% CI 1.02-1.73) and intravenous furosemide use (OR 1.58, 95% CI 1.10-2.28) were associated with early repeat hospitalization for fluid overload. Higher systolic BP on admission (OR 0.992, 95% 0.986-0.998) and diuretic at discharge (OR 0.50, 95% CI 0.26-0.98) reduced early hospitalization for fluid overload. CONCLUSION: Patients at-risk of early repeat hospitalization for fluid overload may be identified using these risk factors for targeted interventions.

Improving Diabetic Wound-Healing Outcomes With Topical Growth Factor Therapies.

CONTEXT: Diabetes mellitus is associated with morbid complications such as diabetic foot ulcers (DFUs) that may lead to amputations or mortality if not managed adequately. OBJECTIVE: New adjunctive interventions to treat diabetic wounds include topical biologics and growth factors. This study aims to evaluate their efficacy in improving wound-healing outcomes and safety. METHODS: Comprehensive database searches of MEDLINE via PubMed, EMBASE, and Cochrane were performed from inception to December 2022. Three independent researchers selected the studies. Randomized controlled trials that compared the use of a topical biologic growth factor-containing regimen to other biologics or standard of care (SOC) were included. This review followed PRISMA guidelines. Risk of bias analysis was performed using the Jadad scale. Network meta-analysis was performed. Treatments were grouped into common nodes based on the type of biologic agent. Primary outcomes of interest were healing rate and time to wound closure. Secondary outcomes included wound infection, serious adverse events (AEs), and amputation rate. RESULTS: Human umbilical cord (HUC) was associated with the highest cure, followed by recombinant human epidermal growth factor (hEGF). A significantly greater reduction in the time to cure DFUs was seen in HUC, hEGF, and fibroblast growth factor (FGF). There was a significantly lower risk of AEs when platelet-rich plasma (PRP) was administered. CONCLUSION: HUC, hEGF, and FGF are promising topical biologics with statistically significant primary outcomes compared to SOC, while PRP is effective in reducing ulcer-related AEs. HUC has been found to be the most effective in terms of cure rate and a reduction in time to cure.

Hospital readmissions for fluid overload among individuals with diabetes and diabetic kidney disease: risk factors and multivariable prediction models.

AIMS: Hospital readmissions due to recurrent fluid overload in diabetes and diabetic kidney disease can be avoided with evidence-based interventions. We aimed to identify at-risk patients who can benefit by developing risk prediction models for readmissions for fluid overload in people living with diabetes. METHODS: Single-center retrospective cohort study of 1531 adults with diabetes and hospitalized for fluid overload, including congestive heart failure, pulmonary edema and generalized edema, between 2015 and 2017. The multivariable regression models for 30-day and 90-day readmission for fluid overload were compared with the LACE score for discrimination, calibration, sensitivity, specificity and net reclassification index (NRI). RESULTS: Readmissions for fluid overload within 30 days and 90 days occurred in 8.6% and 17.2% of patients with diabetes, and 8.2% and 18.3% of patients with diabetic kidney disease, respectively. After adjusting for demographics, comorbidities, clinical parameters, and medications, a history of alcoholism (HR 3.85, 95% CI 1.41-10.55) and prior hospitalization for fluid overload (HR 2.50, 95% CI 1.26- 4.96) were independently associated with 30-day readmission in patients with diabetic kidney disease, as well as in individuals with diabetes. Additionally, current smoking, absence of hypertension and high-dose intravenous furosemide were also associated with 30-day readmission in individuals with diabetes. Prior hospitalization for fluid overload (HR 2.43, 95% CI 1.50-3.94), cardiovascular disease (HR 1.44, 95% CI 1.03-2.02), eGFR ≤45 ml/min/1.73 m2 (HR 1.39, 95% CI 1.003-1.93) were independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide at discharge reduced 90-day readmission in diabetic kidney disease while high-dose intravenous furosemide predicted 90-day readmission in diabetes. The clinical and clinico-psychological models for 90-day readmission in individuals with diabetes and diabetic kidney disease had better discrimination and calibration than the LACE score. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetes were 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease were 5.6% and 38.9%, respectively. CONCLUSION: The risk models can potentially be used to identify patients at-risk of readmission for fluid overload for evidence-based interventions such as patient education or transitional care programs to reduce preventable hospitalizations. .

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.

BACKGROUND: The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management. OBJECTIVE: This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application. METHODS: We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed. RESULTS: A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice. CONCLUSIONS: Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts.

Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus.

BACKGROUND: Inconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA1c values. Trajectory analysis can incorporate the effects of HbA1c variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA1c trajectories from diabetes diagnosis and the occurrence of HHF are scarce. METHODS: This is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA1c trajectories. Association between HbA1c trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model. RESULTS: 5 distinct HbA1c trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA1c trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA1c clustering results. CONCLUSIONS: Findings suggest that tracking the evolution of HbA1c with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA1c trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA1c trajectories and HHF occurrence from diabetes diagnosis time.

Real-World Systolic and Diastolic Blood Pressure Levels and Cardiovascular Mortality in Patients With Type 2 Diabetes-Results From a Large Registry Cohort in Asia.

BACKGROUND: Elevated blood pressure (BP) is associated with increased risk of cardiovascular mortality. However, there is ongoing debate whether intensive BP lowering may paradoxically increase the risk of cardiovascular disease (CVD), especially in patients with type 2 diabetes (T2D). We investigated the association of BP with risk of CVD mortality in patients with T2D. METHODS AND RESULTS: We used data on 83 721 patients with T2D from a multi-institutional diabetes registry in Singapore from 2013 to 2019. BP was analyzed as categories and restricted cubic splines using Cox multivariable regression analysis stratified by preexisting CVD and age (<65 years versus ≥65 years). The primary outcome was CVD mortality, determined via linkage with the national registry. Among 83 721 patients with T2D (mean age 65.3 years, 50.6% women, 78.9% taking antihypertensive medications), 7.6 per 1000 person-years experienced the primary outcome. Systolic BP had a graded relationship with a significant increase in CVD mortality at levels >120 to 129 mm Hg. Diastolic BP levels >90 mm Hg were significantly associated with CVD mortality in those aged ≥65 years. In addition, diastolic BP <70 mm Hg was associated with a significantly higher risk of CVD mortality in all patients. CONCLUSIONS: In patients with T2D, clinic systolic BP levels ≥130 mm Hg or diastolic BP levels ≥90 mm Hg are associated with higher risk of CVD mortality. Diastolic BP <70 mm Hg is also associated with the risk of adverse CVD outcomes, although reverse causality cannot be ruled out.

Associations Between Mean HbA1c, HbA1c Variability, and Both Mortality and Macrovascular Complications in Patients with Diabetes Mellitus: A Registry-Based Cohort Study.

Background: We investigate the association between mean HbA1c, HbA1c variability, and all-cause mortality and diabetes-related macrovascular complications in patients with diabetes. Methods: We performed a retrospective cohort study using patients present in the Singapore Health Services diabetes registry (SDR) during 2013 to 2014. We assessed mean HbA1c using three models: a baseline mean HbA1c for 2013-14, the mean across the whole follow-up period, and a time-varying yearly updated mean. We assessed HbA1c variability at baseline using the patient's HbA1c variability score (HVS) for 2013-14. The association between mean HbA1c, HVS, and 6 outcomes were assessed using Cox proportional hazard models. Results: We included 43,837-53,934 individuals in the analysis; 99.3% had type 2 diabetes mellitus. The data showed a J-shaped distribution in adjusted hazard ratios (HRs) for all-cause mortality, ischemic heart disease, acute myocardial infarction, peripheral arterial disease, and ischemic stroke, with an increased risk of developing these outcomes at HbA1c <6% (42 mmol/mol) and ≥8% (64 mmol/mol). With the addition of HVS, the J-shaped distribution was maintained for the above outcomes, but HRs were greater at HbA1c <6.0% (42 mmol/mol) and reduced at HbA1c ≥8.0% (64 mmol/mol) when compared to models without HVS. The risk for all outcomes increased substantially with increasing glycaemic variability. Conclusion: Both low (<6.0% [42 mmol/mol]) and high (≥8.0% [64 mmol/mol]) levels of glycaemic control are associated with increased all-cause mortality and diabetes-related macrovascular complications. Glycaemic variability is independently associated with increased risk for these outcomes. Therefore, patients with stable glycaemic level of 6-8% (42-64mmol/mol) are at lowest risk of all-cause mortality and diabetes-related macrovascular complications.

Telemonitoring With a Connected Glucose Meter Improves Glycemia Among People With Insulin-Treated Type 2 Diabetes.

BACKGROUND: Delayed initiation and inadequate titration remain critical challenges to optimizing insulin therapy in type 2 diabetes (T2D). We aimed to study whether hemoglobin A1c (HbA1c) can be lowered in people with insulin-treated T2D using telemonitoring. METHODS: This single-center study recruited adults with greater than or equal to six months of diabetes, greater than or equal to three months of insulin therapy, HbA1c ≥8.5% and ≤12.5%, and body mass index (BMI) ≤40 kg/m2. All participants received a connected glucose meter and the accompanying smartphone application. Participants sent weekly blood glucose (BG) diary to their primary endocrinologist via email. Adjustments in insulin doses were communicated to the participants. HbA1c, proportion of BG readings in range (70-180 mg/dL, PIR), below range (<70 mg/dL, PBR) and above range (>180 mg/dL, PAR), and glycemic variability as the coefficient of variation (% CV) were measured at baseline, week 12, and week 24 and compared using repeated-measures analysis of variance (ANOVA) or Friedman's ANOVA. RESULTS: We recruited 40 people (55% women). Mean age was 57.9 years, BMI 27.8 kg/m2, and baseline HbA1c 9.8% (83.7 mmol/mol). Mean HbA1c improved by 1.7%, % CV reduced from 32.9% to 30.7%, PIR increased from 58.8% to 67.1% (all P <.01) by week 24, without any change in PBR. This was achieved with a 0.04 U/kg/d median increase in total daily dose of insulin and 0.9 kg weight gain over 24 weeks. CONCLUSION: Telemonitoring and titration of insulin using a connected glucose meter resulted in significant improvements in glycemia, characterized by a reduction in HbA1c, increase in PIR, and reduction in glycemic variability without any increase in hypoglycemia.

Non-steroidal anti-inflammatory drugs in chronic kidney disease and risk of acute adverse kidney events according to route of administration.

BACKGROUND: Topical non-steroidal anti-inflammatory drugs (NSAIDs) have lower risks for cardiovascular disease and gastrointestinal adverse effects compared to oral NSAIDs, but there are little data regarding their kidney risks in chronic kidney disease (CKD). We evaluated the risk of adverse acute kidney outcomes in CKD according to route of NSAID administration. METHODS: Retrospective cohort study of adults with CKD (eGFR less than 60 ml/min/1.73 m2) who received prescriptions between 2015 and 2017 from a major healthcare cluster in Singapore. The adverse acute kidney outcomes were acute kidney injury (AKI) and need for nephrology specialist consult within 30 days. RESULTS: Among 6298 adults with CKD (mean age 72.1 ± 13.3 years and eGFR 41.9 ± 12.2 ml/min/1.73 m2), systemic and topical NSAIDs were prescribed in 16.7% and 32.0%, respectively. Incident AKI (any severity), KDIGO Stage 2 or 3 AKI, and need for nephrology specialist consult occurred in 16.7%, 2.6%, and 10.6% of the study cohort, respectively. After adjusting for age, diabetes, recent cardiovascular hospitalization, baseline eGFR, RAAS blocker and diuretic, systemic NSAIDs, and topical NSAIDs, compared with the no-NSAID group, were independently associated with incident AKI [adjusted OR 1.77 (95% CI 1.46-2.15) and 1.38 (1.18-1.63), respectively]. Moderate and severe AKI (adjusted OR 1.68, 95% CI 1.09-2.58, p = 0.02) and need for nephrology consults (adjusted OR 1.41, 95% CI 1.09-1.82, p = 0.008) were also increased in systemic NSAIDs. CONCLUSION: Among adults with CKD, both systemic and topical NSAIDs were independently associated with acute adverse kidney outcomes.

Protocol update and baseline characteristics for the TRIal to slow the Progression of Diabetes (TRIPOD) randomized controlled trial.

BACKGROUND: Type 2 diabetes (T2D), a major risk factor for cardiovascular disease and other adverse health conditions, is on the rise in Singapore. TRIPOD is a randomized controlled trial aimed to determine whether complementing usual care with an evidence-based diabetes management package (DMP) -comprising access to an evidence-based app, health coaching, pedometer, glucometer and weighing scale, with or without a financial rewards scheme (M-POWER rewards), can improve mean HbA1c levels at months 6 and 12. METHODS: The protocol was published in Trials, accessible via https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-019-3749-x 1. This manuscript updates the protocol with changes to the study design due to challenges with recruitment and presents baseline characteristics. Key updates include changing the arm allocation ratio from 1:1:1 (Arm 1-Usual Care: Arm 2-DMP: Arm 3-DMP+M-POWER rewards) to 10:1:10, the sample size from 339 to 269, the intervention period from two to one year, and the primary hypothesis to focus solely on differences between Usual Care and DMP+M-POWER rewards. Recruitment for the study began on 19 October 2019 and ended on 4 June 2022. RESULTS: The average age of participants was 55.0 (SD9.7) years old and 64.2% were male. The majority of participants (76.8%) were Chinese, 4.9% Malay and 18.3% Indian and of other ethnicities. 67.0% had a monthly household income of SGD$4000 or more. The mean baseline HbA1c was 8.10% (SD 0.95) and the mean body mass index was 26.8 kg/m2 (SD 5.3). DISCUSSION: The final participant completed month 12 follow-up data collection on 8 June 2023. All pre-planned analyses will be conducted and final results reported. TRIAL REGISTRATION: ClinicalTrials.gov NCT03800680 . Registered on 11 January 2019.

Comparison of existing methods of low-density lipoprotein cholesterol estimation in patients with type 2 diabetes mellitus.

Introduction: Elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor for atherosclerotic cardiovascular disease (ASCVD). Direct LDL-C measurement is not widely performed. LDL-C is routinely calculated using the Friedewald equation (FLDL), which is inaccurate at high triglyceride (TG) or low LDL-C levels. We aimed to compare this routine method with other estimation methods in patients with type 2 diabetes mellitus (T2DM), who typically have elevated TG levels and ASCVD risk. Method: We performed a retrospective cohort study on T2DM patients from a multi-institutional diabetes registry in Singapore from 2013 to 2020. LDL-C values estimated by the equations: FLDL, Martin/Hopkins (MLDL) and Sampson (SLDL) were compared using measures of agreement and correlation. Subgroup analysis comparing estimated LDL-C with directly measured LDL-C (DLDL) was conducted in patients from a single institution. Estimated LDL-C was considered discordant if LDL-C was <1.8mmol/L for the index equation and ≥1.8mmol/L for the comparator. Results: A total of 154,877 patients were included in the final analysis, and 11,475 patients in the subgroup analysis. All 3 equations demonstrated strong overall correlation and goodness-of-fit. Discordance was 4.21% for FLDL-SLDL and 6.55% for FLDL-MLDL. In the subgroup analysis, discordance was 21.57% for DLDL-FLDL, 17.31% for DLDL-SLDL and 14.44% for DLDL-MLDL. All discordance rates increased at TG levels >4.5mmol/L. Conclusion: We demonstrated strong correlations between newer methods of LDL-C estimation, FLDL, and DLDL. At higher TG concentrations, no equation performed well. The Martin/Hopkins equation had the least discordance with DLDL, and may minimise misclassification compared with the FLDL and SLDL.